Cross-Platform Chinese Offensive Comment Detection via Dual-Threshold Hard Example Mining
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2606.27629 (cs)
[Submitted on 26 Jun 2026]
Title:Cross-Platform Chinese Offensive Comment Detection via Dual-Threshold Hard Example Mining
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Abstract:Cross-platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual-threshold hard mining method to address this. First, the clean-Chinese-base RoBERTa is finetuned on COLD to establish a binary baseline for fair comparison. Second, a three-class fine-labeled test set covering Weibo, Xiaohongshu, Tieba, and Zhihu is constructed, domain distances from the source are quantified using Jaccard and Proxy-A Distance, as well as the degradation bottleneck of the baseline under domain shift is systematically revealed. Herein, a dual threshold hard example mining strategy is proposed. High- and low-confidence error-prone samples are filtered from unlabeled corpora by prediction confidence. The model is secondarily finetuned under implicit contexts with merely a small set of manually labeled hard examples, realizing low-cost cross-platform domain adaptation. Experiments reveal significant performance gains of the optimized model across four platforms.
| Comments: | 10 pages, 7 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| MSC classes: | 68T50, 68U15, 91F10 |
| ACM classes: | I.2.7; I.2.6; H.3.4 |
| Cite as: | arXiv:2606.27629 [cs.CL] |
| (or arXiv:2606.27629v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27629
arXiv-issued DOI via DataCite (pending registration)
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